Web mining for web personalization

By M. Eirinaki, Michalis Vazirgiannis. TOIT, 2003

1,039 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Frequent pattern mining: current status and future directions (2007, background): “Association rules discovered for pages that are often visited together can reveal user groups (Eirinaki and Vazirgiannis 2003) and cluster web pages.”
Mining interesting knowledge from weblogs: a survey (2005, background): “In [64,74] privacy concerns linked to Web Personalization are discussed.”
Research-paper recommender systems: a literature survey (2015, background): “For some comprehensive surveys about user modeling in the context of web personalization, refer to [237,238].”
A federated graph neural network framework for privacy-preserving personalization (2021, methodology): “the local gradients on user clients based on their L1-norm with a threshold δ , and apply a local differential privacy (LDP)(36) module with zero-mean Laplacian noise to the unified gradients to achieve better user privacy protection, which are formulated as follows: gi = clip(gi,δ )+Laplace(0,λ ), ”
Monitoring web browsing behavior with differential privacy (2014, background): “• detecting interesting, new contents on the web [13, 24] ,”